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PROJECT_STATUS.md

Phase 15 - Living Project Status Report

This document communicates the active engineering state, recent architectural direction, open research questions, and development priorities of Project Hermit (v0.1.9).

1. Active Development Focus

Project Hermit is an active, evolving software system. Recent work has transitioned the project from raw payload optimization into measurement integrity, system hardening, and resource-bounded execution safety.

graph LR
    A[v0.1.7: AST Math Mutator] --> B[v0.1.8: Dual-Phase Scheduling]
    B --> C[v0.1.9: Measurement Integrity & Hardening]
    C --> D[Active Focus: Containerized Isolation & Multi-Language Mutators]
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Recent Engineering Milestones Completed in v0.1.9:

  1. Fork-Based Grandchild Sandbox Memory Measurement: Replaced cumulative RUSAGE_CHILDREN checks with isolated grandchild process peak RSS monitoring, resolving the 0.0 KB RAM reporting anomaly.
  2. 6-Merge Sequence Cycle Detection: Eliminated the strategy overcorrection bug by tracking strategy recency patterns (A-B-A-B) instead of total all-time strategy counts.
  3. UTC Timezone Unification: Unified logging, database records, and thought ledgers under UTC timestamps.
  4. Token Cost Budgeting: Integrated per-skill token budgets (skill_budgets) to gate out expensive synthesis loops on complex untouched functions.
  5. Automated Database Backups & Integrity Verification: Enforced startup database health checks (PRAGMA integrity_check) and automated 30-minute VACUUM INTO backups (hermit_memory_backup_*.db).

2. Active Research & Open Engineering Questions

  • Dynamic Weight Calibration in Pareto Scoring: How can multi-objective Pareto scoring dynamically adjust weights (latency vs. memory vs. complexity) based on hardware constraints without introducing optimization bias?
  • Cross-Skill Refactoring Synergy: How can the autonomous daemon identify opportunities to merge duplicate code blocks across separate payload skills into shared utility routines?
  • Cross-Platform Sandboxing: How can RAM-disk sandbox isolation be cleanly ported to macOS or Windows environments using lightweight container fallbacks?

3. Current System Priorities

  1. Maintain Measurement Integrity: Preserve exact harness freezing and fork-based RAM measurement to ensure baseline comparable scores across optimization cycles.
  2. Token Efficiency: Expand deterministic AST mutators (math_mutator.py, qubo_mutator.py) to reduce reliance on expensive LLM synthesis calls.
  3. Continuous Downstream Regression Testing: Enforce mandatory regression testing on static dependency edges (skill_dependencies) prior to candidate merges.